Thoughts on systems and organizations

Month: March 2015

When company management and their external advisers talk about large-scale fundamental change, the typical analogy used is that of the butterfly turning from a pupa to an adult with wings. The desire is to do a “transformation” – dramatically, rapidly and irreversibly changing the company.

Transformations such as organization chart changes or changes in ways of working are often done using roughly the following logic

Analyze current situation in depth (by the top team)

Develop blueprint for new ways of working (by the top team)

Announce new ways of working and “Day-1″ to the organization (by the top team)

… and then the organization tries to implement these blueprints in the next months, typically in a chaotic firefighting process

When it comes to organizational change and transformations, an often quoted statistic is that ~70% of the changes fail – no butterfly emerges from the pupa. Reasons for failure are typically labelled as “resistance to change” – the organization does not embrace the new ways of working developed by the top team in isolation during the preparation phase.

Interestingly, a similar failure rate is quoted for software projects using the “waterfall approach”. The waterfall approach is similar to the model for organizational change depicted above – first design the software, then code it, and then hand it over to the user to see what (if anything) happens.

In software projects, companies are increasingly turning to agile methods for coding. The idea is to break the long development phase into shorter cycles with the aim of having a working product (even if crude and unfinished) after each. The end users can experience the product and are encouraged to give new guidance on desired features – whereas in the waterfall paradigm these “changing customer requirements” were seen as the worst type of evil. Success rates are up to three times higher for agile projects compared to waterfall methods.

What if similar methods were applied in organization design and business transformations? What if we would let go of the notion that the butterfly magically emerges from the pupa in one go, and instead accept that in reality, the change is actually a very gradual process, taking many days (long time in insect life), and crucially, one in which the butterfly actually is in a living and functioning state at all times during the transformation? Recent science supports this idea as seen in the images below (from Lowe et al., 2013)

So next time you are planning a transformation, don’t focus just on the butterfly, but on all the intermediary stages in between. How to involve the whole organization into gradually and iteratively changing towards a better state? How to avoid the embarrassing moment when you announce something developed in isolation by the top team to the whole organization, and experience frustration, fear and confusion instead of the “aha moment” you were hoping for.

As a part of our research project described earlier (in this post), we hope to act as a catalyst for change within Finnish home care industry. Home care in Finland suffers from the problem of limited nursing resources and increasing number of (mainly elderly) patients, which currently manifest themselves in low satisfaction among both patients and nurses.

We are now meeting with dozens of companies providing home care (ranging from the very small to the very large), to highlight a way to do things differently: the Buurtzorg model that has been successfully applied in the Netherlands. Buurtzorg has implemented a non-hierarchical leadership model built around self-organizing nurse teams supported by a very lean central HQ. Since their founding in 2006, they have scaled to ~8000 nurses today. Both patients and nurses report increased satisfaction, and the efficiency has skyrocketed as well. For more information, check out e.g., this case study.

We are finding that the idea of letting go of hierarchy, having self-organizing nurse teams, truly embracing the customer context (not optimizing minutes per visit, but customer health and social connections) and fast scaling of the business as done by Buurtzorg sound attractive to the companies we are talking with.

Implementing this model at scale (in home care and beyond) in Finland could provide efficiency gains well above those that any structural solutions (e.g., SOTE) could bring. When treated holistically and with respect using the Buurtzorg approach, patients need less hours of care in total and are less likely to need expensive hospital care.

Together with my colleagues from Finland Futures Research Center, Aalto and Hanken, we have been busy in the past weeks putting together a research agenda and securing the participation of interested companies.

The theme is non-hierarchical companies – companies that have abandoned the belief in hierarchies and are instead exploring a way of running a company based on self-organizing, emergent direction setting, intrinsic motivation and emphasis on culture. At the heart there is a fundamentally different belief about the nature of humans. These companies believe humans are inherently good, trustworthy and wanting to do the right things. If you believe in that, then a lot of the policies, middle management coordination, processes, control mechanisms and other ingredients of bureaucracy that are evident everywhere actually become irrelevant.

Elections for the parliament are approaching again in Finland, and it’s time to think on how to cast your precious vote. The popular method seems to be to try out one or more of the voting aid websites (such as YLE Vaalikone or HS Eduskuntavaalit 2015 vaalikone), which typically present the voter with ~40 Likert-style questions on various contemporary political issues, and then rank the candidates and parties by the degree of match between your opinions and theirs.

Should you then vote for the candidate closest to you? Absolutely not!

At first sight, it would seem to be logical to choose a representative whose opinions match with your. However, this approach ignores the actual mechanism of politics and voting, and results into sub-optimal voting in situations where more than one candidate is being chosen (ie. all elections except for presidency and yes/no questions which are rare). Since your vote is your most valuable democratic asset, it would be wasteful to use it in a sub-optimal way. Here are two alternative approaches I’ve been developing and using during past elections.

Maximum leverage voting

In this approach, one tries to maximize the impact on the current political consensus. This means to have as large as possible effect on the politics practiced by the elected. The approach is as follows: Imagine politics as a n-dimensional space defined by different axes (e.g., right/left, liberal/conservative, green/not-green). Vote for the candidate whose opinion is as far from the average opinion as possible, towards the factor defining your own opinion. However, if the candidate chosen in this way is too extreme / outlier to be considered seriously, choose a candidate closer to the political average.

For example, if the voter thinks that on a scale of 1 to 10 for right vs. left wing politics the current political consensus is around 5, but the voter would like to see politics around 6, he or she would like to the consensus shift to 6. The voter has the options of

Voting for a candidate proposing score 6 politics (Closest candidate, proposed by the voting aid pages), however the impact on the consensus is just a minimal shift towards 6.

Voting for a candidate proposing score 10 politics, where the impact is a larger shift towards 6.

Within party optimization

The second approach is for situations where D’Hondt method is used for tallying the votes. In the system, the each vote cast is primarily given to the party or list, and then each candidate is given a quotient based on his or her rank within the party. The most voted candidate on the list gets a quotient equal to all votes, the second gets 1/2 of the votes and so on.

So what your vote is actually doing is foremost supporting the party, and secondly determining the relative rank of your candidate. To get optimal results under this system, you should

Determine your party based on maximum leverage method (above)

Estimate the number of seats (marked as n) that party will get (e.g., based on previous elections or most recent polls) in your voting district

Estimate the top n+1 candidates within the party by expected votes they will get (again, using e.g., previous election data, candidate-level polls if available, Google Trends search frequency data or any other means)

Select a candidate from within the top n+1 using the maximum leverage method.

So for example, you choose that Party X is best suited for driving the political consensus to your direction. The party has received 2 seats from your voting district for the past few elections and no great change is expected. You estimate that candidates A, B and C are the top-3 likely candidates. Based on maximum leverage, you determine that candidate C is the maximum distance towards your direction from the political center and thus receives your vote.